Brain-Controlled Epidural Spinal Stimulation for Upper-Limb Motor Function after Tetraplegia
Bibliographic record
Abstract
Abstract Spinal cord injury (SCI) disrupts descending motor pathways, leaving individuals with tetraplegia dependent on residual neural connections to generate voluntary movement, with limited recovery despite extensive rehabilitation. Epidural spinal cord stimulation (ESCS) has emerged as a promising neuromodulation strategy that can amplify spinal sensorimotor pathways, enabling residual circuits to respond more effectively to attempted voluntary commands. However, most approaches deliver stimulation continuously rather than in response to volitional intent, limiting the integration of cortical commands and spinal activation that may enhance both neuroprosthetic utility and the potential for recovery. Here, we present an implantable brain-computer interface (BCI) that decodes attempted movement from electrocorticography signals to trigger cervical ESCS during upper-limb motor tasks in an individual with chronic, motor-complete cervical SCI. We demonstrated that both BCI-driven and tonic ESCS immediately enhanced motor function compared to no stimulation, with BCI-ESCS producing greater improvements in grip force and reaching accuracy. Parallel to these assistive effects, BCI-ESCS facilitated corticospinal and spinal excitability after a single session, whereas tonic stimulation did not, suggesting the utility of BCI-driven neuromodulation for activity-dependent plasticity. Four weeks of BCI-ESCS use further drove meaningful improvements in hand motor function exceeding clinical improvement thresholds, with selected gains persisting at one-month follow-up. Together, these findings establish a translational proof-of-concept for an implantable BCI-ESCS, demonstrating the feasibility of intent-driven neuromodulation as a restorative strategy that provides both neuroprosthetic assistance and therapeutic benefit following chronic complete tetraplegia. One-Sentence Summary Implanted brain-controlled spinal stimulation provides neuroprosthetic assistance and drives recovery in chronic tetraplegia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".